000084213 001__ 84213
000084213 005__ 20200117212654.0
000084213 0247_ $$2doi$$a10.1063/1.5036959
000084213 0248_ $$2sideral$$a108655
000084213 037__ $$aART-2018-108655
000084213 041__ $$aeng
000084213 100__ $$aQuintero-Quiroz, C.
000084213 245__ $$aDifferentiating resting brain states using ordinal symbolic analysis
000084213 260__ $$c2018
000084213 5060_ $$aAccess copy available to the general public$$fUnrestricted
000084213 5203_ $$aSymbolic methods of analysis are valuable tools for investigating complex time-dependent signals. In particular, the ordinal method defines sequences of symbols according to the ordering in which values appear in a time series. This method has been shown to yield useful information, even when applied to signals with large noise contamination. Here, we use ordinal analysis to investigate the transition between eyes closed (EC) and eyes open (EO) resting states. We analyze two electroencephalography datasets (with 71 and 109 healthy subjects) with different recording conditions (sampling rates and the number of electrodes in the scalp). Using as diagnostic tools the permutation entropy, the entropy computed from symbolic transition probabilities, and an asymmetry coefficient (that measures the asymmetry of the likelihood of the transitions between symbols), we show that the ordinal analysis applied to the raw data distinguishes the two brain states. In both datasets, we find that, during the EC-EO transition, the EO state is characterized by higher entropies and lower asymmetry coefficient, as compared to the EC state. Our results thus show that these diagnostic tools have the potential for detecting and characterizing changes in time-evolving brain states.
000084213 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/FIS2015-66503$$9info:eu-repo/grantAgreement/ES/MINECO/FIS2015-66503-C3-2-P
000084213 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
000084213 590__ $$a2.643$$b2018
000084213 591__ $$aPHYSICS, MATHEMATICAL$$b5 / 55 = 0.091$$c2018$$dQ1$$eT1
000084213 591__ $$aMATHEMATICS, APPLIED$$b19 / 254 = 0.075$$c2018$$dQ1$$eT1
000084213 592__ $$a0.99$$b2018
000084213 593__ $$aApplied Mathematics$$c2018$$dQ1
000084213 593__ $$aMathematical Physics$$c2018$$dQ1
000084213 593__ $$aStatistical and Nonlinear Physics$$c2018$$dQ1
000084213 593__ $$aPhysics and Astronomy (miscellaneous)$$c2018$$dQ1
000084213 593__ $$aMedicine (miscellaneous)$$c2018$$dQ1
000084213 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000084213 700__ $$0(orcid)0000-0003-1183-349X$$aMontesano, L.$$uUniversidad de Zaragoza
000084213 700__ $$aPons, A.J.
000084213 700__ $$aTorrent, M.C.
000084213 700__ $$aGarcía-Ojalvo, J.
000084213 700__ $$aMasoller, C.
000084213 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000084213 773__ $$g28, 10 (2018), 106307 [6 pp]$$pChaos$$tCHAOS$$x1054-1500
000084213 8564_ $$s1110474$$uhttps://zaguan.unizar.es/record/84213/files/texto_completo.pdf$$yVersión publicada
000084213 8564_ $$s639980$$uhttps://zaguan.unizar.es/record/84213/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000084213 909CO $$ooai:zaguan.unizar.es:84213$$particulos$$pdriver
000084213 951__ $$a2020-01-17-21:22:29
000084213 980__ $$aARTICLE